Recall
AI Second Brain Ecosystem
A modular monolith with an asynchronous pipeline for deep knowledge extraction, featuring a vector-driven knowledge graph and remote content ingestion.
Technologies
Overview
Recall is a comprehensive second brain ecosystem designed to streamline knowledge capture and retrieval. It bridges the gap between raw information gathering and structured learning by employing an async pipeline.
Architecture & Decisions
Built on a modular monolith architecture. Next.js handles the frontend with server-side rendering for SEO and initial load speed. The backend is powered by Node.js/Express, utilizing BullMQ for asynchronous task processing (e.g., website scraping, PDF parsing). Pinecone serves as the vector database for semantic search, and Cloudflare R2 provides cost-effective object storage.
Key Features
Challenges
Managing latency during vector embeddings generation and ensuring the Force-Graph rendered smoothly with hundreds of nodes without freezing the UI.
Lessons Learned
Implementing BullMQ drastically improved my understanding of resilient background job processing and how to decouple heavy AI-processing tasks from the main request thread.
Future Roadmap
Integrating local LLM support to run entirely offline, and adding collaborative workspaces for teams.